Hw2
Kacie Kang
3/20/2018
library(leaflet)
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library(rgeos)## rgeos version: 0.3-26, (SVN revision 560)
## GEOS runtime version: 3.6.1-CAPI-1.10.1 r0
## Linking to sp version: 1.2-5
## Polygon checking: TRUElibrary(ggmap)
library(htmlwidgets)
library(lubridate)##
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## datelibrary(RColorBrewer)
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## calclibrary(sf)## Linking to GEOS 3.6.1, GDAL 2.1.3, proj.4 4.9.3data prepration
incidents <- read.csv("/Users/kk/Desktop/severe_incidents.csv",header = TRUE, stringsAsFactors = FALSE)
# Create the time interval of INCIDENT_DATE_TIME and ARRIVAL_DATE_TIME in seconds
incidents$INCIDENT_DATE_TIME = as.POSIXct(incidents$INCIDENT_DATE_TIME, format = "%m/%d/%Y %I:%M:%S %p")
incidents$ARRIVAL_DATE_TIME = as.POSIXct(incidents$ARRIVAL_DATE_TIME, format = "%m/%d/%Y %I:%M:%S %p")
incidents$RESPONSE_TIME <- incidents$ARRIVAL_DATE_TIME - incidents$INCIDENT_DATE_TIME
incidents <- incidents[c("IM_INCIDENT_KEY", "PROPERTY_USE_DESC","INCIDENT_TYPE_DESC", "TOTAL_INCIDENT_DURATION", "RESPONSE_TIME", "Latitude", "Longitude", "BOROUGH_DESC")]
#install.packages("rgdal")
library(rgdal)## Warning: package 'rgdal' was built under R version 3.4.4## rgdal: version: 1.2-18, (SVN revision 718)
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## Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/proj
## Linking to sp version: 1.2-7nyc <- readOGR("/Users/kk/Desktop/nyc_boro.geojson","OGRGeoJSON", verbose=FALSE)
#incidents<-incidents[(incidents$BOROUGH_DESC==c("1 - Manhattan","2 - Bronx","3 - Staten Island","4 - Brooklyn","5 - Queens") ),]
incidents = subset(incidents, !is.na(Latitude))
#incidents
# Merge the QoG data to Shape Files
incidents$BoroCode <- "5"
incidents$BoroCode[incidents$BOROUGH_DESC=="1 - Manhattan"] <- "1"
incidents$BoroCode[incidents$BOROUGH_DESC=="2 - Bronx"] <- "2"
incidents$BoroCode[incidents$BOROUGH_DESC=="4 - Brooklyn"] <- "3"
incidents$BoroCode[incidents$BOROUGH_DESC=="5 - Queens"] <- "4"
incidents$BoroCode[incidents$BOROUGH_DESC=="3 - Staten Island"] <- "5"
nyc@data <- data.frame(incidents, nyc@data[match(incidents[,"BoroCode"], nyc@data[,"BoroCode"]),])Provide a leaflet map of the severe fires contained in the file severe_incidents.csv. Ignore locations that fall outside the five boroughs of New York City.
Provide at least three pieces of information on the incident in a popup.
# Contents shows in popup
popcontent <- paste("INCIDENT TYPE:",incidents$INCIDENT_TYPE_DESC,"<br/>",
"PROPERTY TYPE:",incidents$PROPERTY_USE_DESC,"<br/>",
"Duration:",signif(incidents$TOTAL_INCIDENT_DURATION/60,0),"mins","<br/>",
"Latitude,Longitude:",incidents$Latitude,
incidents$Longitude,"<br/>"
)
# Draw a simple map of the severe fires contained in the file severe_incidents.csv.
Severe_Fires0 <- leaflet(nyc) %>%
addTiles('http://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}.png') %>%
setView(-73.9949344, 40.7179112, zoom = 10)
Severe_Fires1 <- Severe_Fires0 %>%
addCircles(lng = ~Longitude, lat = ~Latitude, popup = popcontent, col="red")
Severe_Fires1- Layers and Clusters
- Color by Type of Property
# prepare the data
PROPERTY <- incidents$PROPERTY_USE_DESC
incidents$PROPERTY_CATEGORY <- "Other Areas"
incidents$PROPERTY_CATEGORY[PROPERTY=="429 - Multifamily dwelling"] <- "Multifamily dwelling"
incidents$PROPERTY_CATEGORY[PROPERTY=="419 - 1 or 2 family dwelling"] <- "1 or 2 family dwelling"
incidents$PROPERTY_CATEGORY[PROPERTY=="500 - Mercantile, business, other" |
PROPERTY=="599 - Business office"] <- "Business Related Areas"
incidents$PROPERTY_CATEGORY[PROPERTY=="161 - Restaurant or cafeteria" |
PROPERTY=="519 - Food and beverage sales, grocery store"] <- "Food Ralated Areas"
incidents$PROPERTY_CATEGORY[PROPERTY=="900 - Outside or special property, other" |
PROPERTY=="931 - Open land or field" |
PROPERTY=="962 - Residential street, road or residential driveway" |
PROPERTY=="960 - Street, other" |
PROPERTY=="881 - Parking garage, (detached residential garage)"] <- "Food Ralated Areas"
incidents$PROPERTY_CATEGORY[PROPERTY=="174 - Rapid transit station" |
PROPERTY=="210 - Schools, non-adult, other"] <- "Public Areas"
# incidents$PROPERTY_CATEGORY
#update contents of popup
popcontent2 <- paste("INCIDENT TYPE:",incidents$INCIDENT_TYPE_DESC,"<br/>",
"PROPERTY CATEGORY:",incidents$PROPERTY_CATEGORY,"<br/>",
"PROPERTY TYPE:",incidents$PROPERTY_USE_DESC,"<br/>",
"Duration:",signif(incidents$TOTAL_INCIDENT_DURATION/60,0),"mins","<br/>",
"Latitude&Longitude:",incidents$Latitude, incidents$Longitude,"<br/>"
)
pal = colorFactor("Set1", domain = incidents$PROPERTY_CATEGORY)
# palette
color_PROPERTY_CATEGORY = pal(incidents$PROPERTY_CATEGORY)
Severe_Fires2a <- Severe_Fires0 %>%
addCircles(lng = ~Longitude, lat = ~Latitude,
popup = popcontent2,
col=color_PROPERTY_CATEGORY) %>%
addLegend("bottomright",
pal = pal,
values = ~incidents$PROPERTY_CATEGORY,
title = "Affected Property Catagories",
opacity = 0.25)
Severe_Fires2aSevere_Fires2b <- Severe_Fires0 %>%
addCircleMarkers(lng = ~Longitude, lat = ~Latitude,
popup = popcontent2,
col=color_PROPERTY_CATEGORY,
clusterOptions = markerClusterOptions()) %>%
addLegend("bottomright",
pal = pal,
values = ~incidents$PROPERTY_CATEGORY,
title = "Affected Property Catagories",
opacity = 0.25)
Severe_Fires2b- Fire Houses The second data file contains the locations of the 218 firehouses in New York City. Start with the non-clustered map (2b) and now adjust the size of the circle markers by severity (TOTAL_INCIDENT_DURATION or UNITS_ONSCENE seem plausible options). More severe incidents should have larger circles on the map. On the map, also add the locations of the fire houses. Add two layers (“Incidents”, “Firehouses”) that allow the user to select which information to show.
#collecting data
fireshouses <- read.csv("/Users/kk/Desktop/FDNY_Firehouse_Listing.csv",header = TRUE, stringsAsFactors = FALSE)
#fireshouses
popcontent3 <- paste("Facility Name:",fireshouses$FacilityName,"<br/>",
"Facility Address:",fireshouses$FacilityAddress,"<br/>",
"Borough:",fireshouses$Borough,"<br/>")
Severe_Fires3 <- leaflet(nyc) %>%
setView(-73.8, 40.8, zoom = 10) %>%
addTiles('http://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}.png') %>%
addCircles(group="Incidents",
weight=0, radius=~incidents$TOTAL_INCIDENT_DURATION/100, fillOpacity=0.6,
lng = ~Longitude, lat = ~Latitude,
popup = popcontent2,
col=color_PROPERTY_CATEGORY) %>%
addLegend(group="Incidents",
"bottomright",
pal = pal,
values = ~incidents$PROPERTY_CATEGORY,
title = "Affected Property Catagories",
opacity = 0.25) %>%
# Add data layers - Fireshouses
addCircles(group = "Fireshouses",
data = fireshouses,
lng = ~Longitude, lat = ~Latitude,
popup = popcontent3,
col="red") %>%
addMarkers(group = "Fireshouses",
data=fireshouses) %>%
# Layers
addLayersControl(overlayGroups = c("Incidents","Fireshouses"),
options = layersControlOptions(collapsed = TRUE) ) ## Warning in validateCoords(lng, lat, funcName): Data contains 5 rows with
## either missing or invalid lat/lon values and will be ignored## Assuming 'Longitude' and 'Latitude' are longitude and latitude, respectively## Warning in validateCoords(lng, lat, funcName): Data contains 5 rows with
## either missing or invalid lat/lon values and will be ignoredSevere_Fires3- Distance from Firehouse and Response Time README !!! there are still some bugs in 4(a), I am about to calculate the min distance of it, but I dont have more time to fix them, please take a look at the codes and if could, give partial credits. Sorry for the inconvenience. Thank you so much !!!
list1 <- data.frame(ID = 1:nrow(incidents), longitude = incidents$Latitude,latitude =incidents$Longitude)
list2 <- data.frame(ID = 1:nrow(fireshouses), longitude = fireshouses$Latitude, latitude=fireshouses$Longitude)
list1[is.na(list1)] <- 0
list2[is.na(list2)] <- 0
#install.packages("data.table")
library(data.table)
library(geosphere)
#df2$longitude.fix <- as.numeric(levels(df2$lon))[df2$lon]
#df2$latitude.fix <- as.numeric(levels(df2$lat))[df2$lat]
#df1$distance<-distHaversine(df1[,1:2], df2[,1:2])
#list1$distance<-distHaversine(list1[,1:2], list2[,1:2])
for (i in 1:nrow(incidents)) {
a<-as.numeric(list1$longitude[i])
b<-as.numeric(list1$Latitude[i])
for (i in 1:nrow(fireshouses)) {
c<-as.numeric(list2$longitude[i])
d<-as.numeric(list2$Latitude[i])
list1$dist <- min(distm(c(a,b),c(c,d), fun = distHaversine))
}
}
incidents$near_dist <- list1$near_dist
library(ggplot2)
Severe_Fires4a <- ggplot(subset(incidents, RESPONSE_TIME < 1000 & near_dist < 0.03),
aes(x =near_dist/1000, y = RESPONSE_TIME)) +
geom_point(alpha=0.5, size=2) +
geom_smooth(lwd = 1, se = FALSE)+
ylab("RESPONSE_TIME(mins)") +
xlab("Distance_to_Nearest_Firehouse (Euclidean distance of Latitude and Longitude)") +
ggtitle("Distance from Nearest Firehouse and Response Time")+
theme(plot.title = element_text(hjust = 0.5))
Severe_Fires4a
popcontent4 <- paste("INCIDENT TYPE:",incidents$INCIDENT_TYPE_DESC,"<br/>",
"PROPERTY CATEGORY:",incidents$PROPERTY_CATEGORY,"<br/>",
"PROPERTY TYPE:",incidents$PROPERTY_USE_DESC,"<br/>",
"Duration:",signif(incidents$TOTAL_INCIDENT_DURATION/60,0),"mins","<br/>",
"Latitude&Longitude:",incidents$Latitude, incidents$Longitude,"<br/>",
"RESPONSE TIME(mins):", incidents$RESPONSE_TIME,"<br/>")
pal = colorFactor("Set1", domain = incidents$PROPERTY_CATEGORY) # Grab a palette
color_PROPERTY_CATEGORY = pal(incidents$PROPERTY_CATEGORY)
Severe_Fires4b <- Severe_Fires0 %>%
addCircles(lng = ~Longitude, lat = ~Latitude,
radius=~incidents$RESPONSE_TIME,fillOpacity=0.4,
popup = popcontent4,
col=color_PROPERTY_CATEGORY) %>%
addLegend("bottomright",
pal = pal,
values = ~incidents$PROPERTY_CATEGORY,
title = "Affected Property Catagories",
opacity = 0.25)
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